Over the past few years vehicle classification has been widely studied as part of the broader vehicle recognition research area. A vehicle classification system is essential for effective transportation systems (e.g., traffic management and toll systems), parking optimization, law enforcement, autonomous navigation, etc. A common approach utilizes vision-based methods to detect and classify a vehicle in still images and video streams. A human being may be capable of identifying the class of a vehicle with a quick glance at the digital data (image, video) but accomplishing that with a computer is not as straight forward. Several problems such as occlusion, tracking a moving object, shadows, rotation, lack of color invariance, and many more must be carefully considered in order to design an effective and robust automatic vehicle classification system. Much research has been conducted for object classification, but vehicle classification has shown to have its own specific problems, which motivates research in this area.
Not much has been done on vehicle classification from the rear view. For the side view, appearance based methods especially edge-based methods have been widely used for vehicle classification. These approaches utilize various methods such as weighted edge matching, Gabor features, edge models, shape based classifiers, part based modeling, and edge point groups. Model-based approaches that use additional prior shape information have also been investigated in 2D (two-dimensions) and more recently in 3D (three-dimensions).
Vehicle make and model classification from the frontal view has also been investigated (i.e., high resolution, close up frontal view images, neural network classifier), and also (SIFT features). For the rear view, Dlagnekov extends a new license plate recognition system to perform vehicle make and model recognition for video surveillance using a database of partial license plate and vehicle visual description data. Adaboost and cascaded classifiers are used to detect the license plate. Given the license plate, visual features are extracted using two feature-based methods (SIFT and shape context matching) and one appearance-based method (Eigencars). The drawbacks of the proposed system are that it does not perform color inference, is relatively slow, and only the license plate recognition stage is done in real-time.